DS2003: Interactive Plots in plotly

Plotly

plotly is a high-level interface to plotly.js, based on d3.js which provides an easy-to-use UI to generate slick D3 interactive graphics.
Plot work in multiple formats: viewer windows, R Markdown documents, shiny apps
plotly is also backed by a strong community and active development
plotly works with R, python, excel, and others. We will just focus on R in this class.

Why use interactive graphics?

Zooming
Silencing
Hovering
Sliding
Filtering

Difference between interactive and animated graphs

interactive graphs allow the user to manipulate different features of the graph

plotly Resources

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(plotly)

## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout

# read in 'wine.csv' data
wine <- read.csv('data/wine.csv')

head(wine)

##   Type Alcohol Malic  Ash Alcalinity Magnesium Phenols Flavanoids Nonflavanoids
## 1    1   14.23  1.71 2.43       15.6       127    2.80       3.06          0.28
## 2    1   13.20  1.78 2.14       11.2       100    2.65       2.76          0.26
## 3    1   13.16  2.36 2.67       18.6       101    2.80       3.24          0.30
## 4    1   14.37  1.95 2.50       16.8       113    3.85       3.49          0.24
## 5    1   13.24  2.59 2.87       21.0       118    2.80       2.69          0.39
## 6    1   14.20  1.76 2.45       15.2       112    3.27       3.39          0.34
##   Proanthocyanins Color  Hue Dilution Proline
## 1            2.29  5.64 1.04     3.92    1065
## 2            1.28  4.38 1.05     3.40    1050
## 3            2.81  5.68 1.03     3.17    1185
## 4            2.18  7.80 0.86     3.45    1480
## 5            1.82  4.32 1.04     2.93     735
## 6            1.97  6.75 1.05     2.85    1450

wine$Type <- as.factor(wine$Type)

Convert Static Graphs to Interactive Graphs Using `ggplotly`

Let’s look at a static graph comparing flavanoids to proline

static_plot <- wine %>%
  ggplot(aes(Flavanoids, Proline, color = Type)) +
  geom_point() +
  theme_minimal() +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

static_plot

How to create a plotly object

There are two main approaches to initialize a plotly object:
- transforming a ggplot2 object with ggplotly()
- setting up aesthetics mappings with plot_ly() directly

Today’s focus `ggplotly()`

ggplotly() takes existing ggplot2 objects and converts them into interactive plotly graphics. That is, ggplotly() converts your static plots to an interactive web-based version!
This makes it easy to create interactive figures because we are already familiar to the ggplot2 syntax.

# Create an interactive plot of Flavonoids vs Proline

ggplotly(static_plot)

Notes

Interactive plots are great!
Bad Design = Bad Interactive Plots
Follow data-viz best practices

Bar Plot

g <- ggplot(data = wine, aes(x = Type, fill = Type)) + 
     geom_bar(fill = c("#00AFBB", "#E7B800", "#FC4E07")) +
     theme_classic() 

ggplotly(g)

If you want the bar in descending order

g.5 <- count(wine, Type) %>% 
       ggplot(aes(x = reorder(Type, -n), y = n)) +
       geom_bar(fill = c("#00AFBB", "#E7B800", "#FC4E07"), stat = 'identity') +
       theme_classic() +
       xlab('Type')
     
ggplotly(g.5)

Histogram

g2 <- ggplot(wine, aes(x = Flavanoids, fill = as.factor(Type))) + 
      geom_histogram(bins = 15, alpha = 0.6) +
      theme_minimal() +
      scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) 

ggplotly(g2)

Scatterplot

g4 <- ggplot(wine, aes(x = Flavanoids, y = Proline, color = as.factor(Type))) + 
      geom_point() + 
      geom_smooth(method="loess", formula=y~x, se=F) + 
      scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"), name = 'Type') +
      theme_bw() 

ggplotly(g4)

g5 <- ggplot(wine, aes(x = Flavanoids, y = Proline, color = as.factor(Type))) + 
      geom_point() + 
      geom_smooth(method="lm", formula=y~x, se=F) + 
      scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"), name = 'Type') +
      theme_bw()

ggplotly(g5)

Creating interactive graphs directly in `plot_ly`

plot_ly() is the base plotly command to initialize a plot from a dataframe, similar to ggplot() from ggplot2.

import `plotly`

library(plotly)

Bar plot

wine %>% mutate(fType = as.factor(Type)) %>%
  count(fType) %>% #using dplyr create frequency table
  plot_ly(x = ~fType, y = ~n) %>% # initialize graph in plotly, use ~ for aes mappings
  add_bars() %>% #set graph as bar chart
  layout(title = "Freq. of Wine Soil Type",
         xaxis = list(title = "Type"),
         yaxis = list(title = "Frequency"))

Reorder columns

wine %>% mutate(fType = as.factor(Type)) %>%
  count(fType) %>% #using dplyr create frequency table
  mutate(fType = fct_reorder(fType, n, .desc = TRUE)) %>% #order for largest to smallest
  plot_ly(x = ~fType, y = ~n) %>% # initialize graph in plotly, use ~ for aes mappings
  add_bars() %>% #set graph as bar chart
  layout(title = "Freq. of Wine Soil Type",
         xaxis = list(title = "Type"),
         yaxis = list(title = "Frequency"))

Basic Histogram

g6 <- plot_ly(wine, x = ~Flavanoids, type = "histogram")

g6

Flavanoids by Type Histogram

Type1 <- wine %>% filter(Type == 1)
Type2 <- wine %>% filter(Type ==2)
Type3 <- wine %>% filter(Type == 3)

g7 <- plot_ly(alpha = 0.4) %>%
  add_histogram(x = ~Type1$Flavanoids,
                name = 'Type 1',
                opacity = 0.6, 
                marker = list(color = "#00AFBB",
                              alpha = 0.6,
                              line = list(color = "lightgray",
                              width = 2))) %>%
  add_histogram(x = ~Type2$Flavanoids,
                name = 'Type 2',
                opacity = 0.6,
                marker = list(color = "#E7B800",
                              line = list(color = "lightgray",
                              width = 2))) %>%
  add_histogram(x = ~Type3$Flavanoids, 
                name = 'Type 3',
                opacity = 0.6,
                marker = list(color = "#FC4E07",
                              line = list(color = "lightgray",
                              width = 2))) %>%
  layout(barmode = 'overlay',
         title = 'Histogram of flavanoids by soil type',
         xaxis = list(title = 'flavanoids',
                      zeroline = FALSE),
         yaxis = list(title = 'count'))

g7

Scatter plot

fig <- plot_ly(type = 'scatter', mode = 'markers') 

fig <- fig %>%
  add_trace(
    x = Type1$Flavanoids,
    y = Type1$Proline,
    opacity = 0.5,
    marker = list(
      color = "#00AFBB",
      size = 7,
      line = list(
        color = 'lightgray',
        width = .5
      )
    ),
    name = 'Type 1'
  ) 

fig <- fig %>%
  add_trace(
    x = Type2$Flavanoids,
    y = Type2$Proline,
    marker = list(
      color = "#E7B800",
      size = 7,
      line = list(
        color = 'lightgray',
        width = .5
      )
    ),
    name = 'Type 2'
  )

fig <- fig %>%
  add_trace(
    x = Type3$Flavanoids,
    y = Type3$Proline,
    marker = list(
      color = "#FC4E07",
      size = 7,
      line = list(
        color = 'lightgray',
        width = .5
      )
    ),
    name = 'Type 3'
  )

fig <- fig %>%
  layout(title = 'Scatterplot of Flavanoids & Proline',
         xaxis = list(title = 'flavanoids',
                      zeroline = FALSE),
         yaxis = list(title = 'proline'))
fig

3D Scatterplot

fig2 <- plot_ly(type = 'scatter3d', mode = 'markers') 
fig2 <- fig2 %>%
  add_trace(
    x = Type1$Flavanoids,
    y = Type1$Proline,
    z = Type1$Alcalinity,
    opacity = 0.5,
    marker = list(
      color = "#00AFBB",
      size = 7,
      line = list(
        color = 'lightgray',
        width = .5
      )
    ),
    name = 'Type 1'
  ) 

fig2 <- fig2 %>%
  add_trace(
    x = Type2$Flavanoids,
    y = Type2$Proline,
    z = Type2$Alcalinity,
    marker = list(
      color = "#E7B800",
      size = 7,
      line = list(
        color = 'lightgray',
        width = .5
      )
    ),
    name = 'Type 2'
  )

fig2 <- fig2 %>%
  add_trace(
    x = Type3$Flavanoids,
    y = Type3$Proline,
    z = Type3$Alcalinity,
    marker = list(
      color = "#FC4E07",
      size = 7,
      line = list(
        color = 'lightgray',
        width = .5
      )
    ),
    name = 'Type 3'
  )

axx <- list(
  title = "flavanoids"
)

axy <- list(
  title = "proline"
)

axz <- list(
  title = "alcalinity"
)

fig2 <- fig2 %>%
  layout(title = 'Scatterplot of Flavanoids, Proline & Alcalinity',
         scene = list(xaxis = axx,
                      yaxis = axy,
                      zaxis = axz))

fig2

Line Plot

(From plotly examples)

library(plyr)

## ------------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## ------------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:plotly':
## 
##     arrange, mutate, rename, summarise

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following object is masked from 'package:purrr':
## 
##     compact

#create date
tg <- ddply(ToothGrowth, c("supp", "dose"), summarise, length=mean(len))

# create figure
fig <- plot_ly(tg, x = ~dose, y = ~length, type = 'scatter', mode = 'lines', linetype = ~supp, color = I('black')) 

fig <- fig %>% layout(title = 'The Effect of Vitamin C on Tooth Growth in Guinea Pigs by Supplement Type',
         xaxis = list(title = 'Dose in milligrams/day'),
         yaxis = list (title = 'Tooth length'))

fig

DS2003: Interactive Plots in plotly

Natalie Kupperman

Plotly

Why use interactive graphics?

Difference between interactive and animated graphs

plotly Resources

Convert Static Graphs to Interactive Graphs Using ggplotly

How to create a plotly object

Today’s focus ggplotly()

Notes

Bar Plot

If you want the bar in descending order

Histogram

Scatterplot

Creating interactive graphs directly in plot_ly

import plotly

Bar plot

Reorder columns

Basic Histogram

Flavanoids by Type Histogram

Scatter plot

3D Scatterplot

Line Plot

Convert Static Graphs to Interactive Graphs Using `ggplotly`

Today’s focus `ggplotly()`

Creating interactive graphs directly in `plot_ly`

import `plotly`